176 citations · 254 across the 7 of their papers we have counts for
4 papers · 1 filter
Evaluating Deep Learning-based Melanoma Classification using Immunohistochemistry and Routine Histology: A Three Center Study
Christoph Wies, Lucas Schneider, Sarah Haggenmueller +6
Pathologists routinely use immunohistochemical (IHC)-stained tissue slides against MelanA in addition to hematoxylin and eosin (H&E)-stained slides to improve their accuracy in dia…
Using Multiple Dermoscopic Photographs of One Lesion Improves Melanoma Classification via Deep Learning: A Prognostic Diagnostic Accuracy Study
Achim Hekler, Roman C. Maron, Sarah Haggenmüller +28
Background: Convolutional neural network (CNN)-based melanoma classifiers face several challenges that limit their usefulness in clinical practice. Objective: To investigate the im…
Multi-domain stain normalization for digital pathology: A cycle-consistent adversarial network for whole slide images
Martin J. Hetz, Tabea-Clara Bucher, Titus J. Brinker
The variation in histologic staining between different medical centers is one of the most profound challenges in the field of computer-aided diagnosis. The appearance disparity of…
Benchmarking common uncertainty estimation methods with histopathological images under domain shift and label noise
Hendrik A. Mehrtens, Alexander Kurz, Tabea-Clara Bucher +1
In the past years, deep learning has seen an increase in usage in the domain of histopathological applications. However, while these approaches have shown great potential, in high-…